2018/12/07 by Myungjoon Kim, Kim, Myungjoon, Byung Chul Yeo +6
Chemical Engineering · Energy · Materials Science · Physics and Astronomy · #Ammonia Synthesis and Nitrogen Reduction #CO2 Reduction Techniques and Catalysts #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #cond-mat.mtrl-sci
paper · pdf · doi:10.48550/arxiv.1812.02949
We recently found errors in some of our computational results, and are now in the process of revising them. Since these results may confuse the readers, we would like to withdraw in the meantime, and to resubmit the new version after the revision processes completed
openalex publication_date 2018/12/07 · arxiv created 2019/03/27 · arxiv updated 2019/03/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The catalyst development for N2 electroreduction reaction (NRR) with low onset potential and high Faradaic efficiency is highly desired, but remains challenging. Machine learning (ML) recently emerged as a complementary tool to accelerate material discovery; however a ML model for NRR has yet to be developed. Here, we develop and report slab-graph convolutional neural network (SGCNN), an accurate and flexible ML model that is applicable to catalytic surface reactions. With the self-accumulated database of 2,699 surface calculations, SGCNN predict binding energies, ranging over 8 eV, of five key adsorbates (*H, *N2, *N2H, *NH, *NH2) related to NRR performance with the mean-absolute-error of only 0.23eV. Unlike previously available models, SGCNN avoids using ab initio level inputs, instead is solely based on elemental properties that are all readily available in Periodic-Table-of-Elements; true accelerations can be realized. t-distributed stochastic neighbor embedding (t-SNE) analysis reveals that binary intermetallics of averaged d-electron occupation between 4 and 5 could potentially lower the onset potential in N2 electroreduction.